from __future__ import division from __future__ import print_function import argparse import cPickle import os import random import time import numpy import paddle import paddle.dataset.imdb as imdb import paddle.fluid as fluid import paddle.fluid.profiler as profiler word_dict = imdb.word_dict() def crop_sentence(reader, crop_size): unk_value = word_dict['<unk>'] def __impl__(): for item in reader(): if len([x for x in item[0] if x != unk_value]) < crop_size: yield item return __impl__ def lstm_net(sentence, lstm_size): sentence = fluid.layers.fc(input=sentence, size=lstm_size, act='tanh')
from __future__ import print_function import argparse import cPickle import os import random import time import numpy import paddle import paddle.dataset.imdb as imdb import paddle.fluid as fluid import paddle.batch as batch import paddle.fluid.profiler as profiler word_dict = imdb.word_dict() def crop_sentence(reader, crop_size): unk_value = word_dict['<unk>'] def __impl__(): for item in reader(): if len([x for x in item[0] if x != unk_value]) < crop_size: yield item return __impl__ def get_model(args): lstm_size = 512